Enterprises face the wrath of the government for taking part in environmental conservation and adoption of sustainable initiatives along with customer demands. Therefore, enterprises are forced to adopt sustainable supply chain practices (SSCPs), which leads to competitive advantage. Now, sustainable supply chain management (SSCM) is a management process that promotes the adoption of eco-friendly activities in conventional supply chains (SCs). Enterprises in India are under tremendous pressure to include SSCPs into their conventional SCs. The goal of this paper is to evaluate the barriers for the implementation of SSCPs into Indian Micro, Small and Medium Enterprises (MSMEs).
This study aims to identify critical barriers for adoption of SSCPs in the textile MSME SCs located in Eastern India, Odisha with the help of interpretive structural modeling (ISM).
The paper develops a framework for the evaluation of barriers to the adoption of SSCP in the textile SC. This paper also provides appropriate suggestive measures to deal with the barriers and overcome the same to attain a sustainable textile SC.
Opportunities exist for extension of this research on wider geographical area. In addition to this, some other quantitative modeling approaches can be applied, like analytical hierarchy process, to prioritize the barriers.
The framework offers help to SC managers in their decision-making process by enabling them to analyze the barriers and ways to overcome them.
The paper deals with a particular geographical area where such kinds of studies are rare. The proposed framework provides a foundation for further research.
Panigrahi, S. and Rao, N. (2018), "A stakeholders’ perspective on barriers to adopt sustainable practices in MSME supply chain", Research Journal of Textile and Apparel, Vol. 22 No. 1, pp. 59-76. https://doi.org/10.1108/RJTA-07-2017-0036Download as .RIS
Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited
Sustainable supply chain management (SSCM) is an organizational concept that requires effective collaboration among all its stakeholders. It not only encourages ecological balance but also promotes waste reduction, cost minimization and gaining market share.
Indian micro, small and medium enterprises (MSMEs) contribute significantly to the economy. At least 45 per cent of the industrial output, 40 per cent of exports, 42 million job opportunities and 8,000 quality products are manufactured for the local and the global markets (Mathiyazhagan et al., 2013). MSMEs have the potential to revolutionize a country’s economic condition due to their rhetorical ability to bring about innovations leading to the success of these enterprises (Madrid-Guijarro et al., 2009). As the MSMEs are a significant entity in the economy, they have a huge impact on the ecosystem. The growth of MSMEs energizes the economy of developing nations, as these enterprises account for 50 per cent of the production capacity in the economy (Eniola and Entebang, 2015). Big corporations understand the merits of adopting anti-pollution manufacturing strategies (Hussey and Eagan, 2007); however, (Epstein and Roy, 2000) MSMEs are yet to realize the importance of environmental conservation that can lead to higher efficiency, low cost and profitability. According to Van Hemel and Cramer (2002) MSMEs do not consider environmental concerns as their duty because there have not been significant reasons that justify the benefits earned from it. There is a dearth of alternate innovative product designing strategies for the MSMEs that lead to environmental protection.
India ranks itself among the top ten exporters of apparel in the world. India ranks second in the production of cotton and silk and leads the world in jute production. Odisha, that lies in the Eastern part of India, has a rich textile heritage; the handlooms of Odisha have gained worldwide acclaim for their unique design and good quality.
2. Literature review
Textile enterprises are one of the most important sectors that a nation must focus on owing to its need and the direct effect on the economy. The wastewaters from textiles contain dispersing agents, pigments, inorganic salts, dyes, resins, chelating agents, biocides, surfactants, etc., and have a high chemical oxygen demand (COD) (Dasgupta et al., 2015). These effluents if left untreated or scantily treated can damage the water bodies and also the aquatic ecosystem (Reddy et al., 2014; Angelis-Dimakis et al., 2016). Textile production process involves high thermal energy requirement along with huge chemical usage which generates harmful emissions and solid wastes (Hasanbeigi et al., 2012). Cleaner production approaches are required for environmental efficiency, green manufacturing and sustainable production. Some of the major causes of pollution are exhaustive resource utilization and obsolete manufacturing strategies (Hilson, 2000). The textile enterprises are one among the largest consumers of industrial water (wet processing operations) and utilize gallons of freshwater. There is a lack of knowledge and training regarding proper handling of the chemicals used during textile production (Börjeson et al., 2015; Bostrom et al., 2011, Scruggs and Ortolano, 2011). The adverse environmental effects from the textile enterprises are high energy utilization in the production of fibers, yarn and for operations, such as washing and drying; consumption of water and other chemicals in the production process and solid wastes generated from the carbon footprints rising out of transportation in the supply chain (SCs) (Draper et al., 2007; DEFRA, 2008; Kocabas et al., 2009; Beton et al., 2011; Gwilt and Rissanen, 2011; Fletcher, 2013; Gardetti and Torres, 2013; Vajnhandl and Valh, 2014). Authors have presented that the existing regulatory frameworks for textile SCs lack global applicability, that is the nature of SCs vary from one nation to other (Eriksson et al., 2009). The authors have suggested that the adoption and implementation of SSCP in the SC of the MSMEs also requires help from experienced professionals who are capable of taking sensible and legitimate decisions (Lai et al., 2008).
India is likely to become the most populated country with almost 1.6 billion population by 2050, surpassing China. Hence, textile enterprises have an opportunity for massive growth in the upcoming years to meet the consumer need (Hubacek et al., 2007). The authors have conducted an empirical study on the green supply chain management (GSCM) practices in the Indian MSMEs and conclude that MSMEs are subjected to pressures from stakeholders to adopt these practices in their SCs (Mohanty and Prakash, 2014). Authors observed that steps toward innovation in MSMEs of India are mostly inspired by the owners and also by collaborating with larger firms (Subrahmanya, 2005; Kumar and Subrahmanya, 2010). Nanda and Singh (2009) presented the five crucial factors affecting the technology development of the Indian MSMEs, namely, human resources and top management commitment, technology, regulatory systems, market interaction and potential research. The growth of Indian MSMEs is directly affected by the entrepreneur’s educational level, technological support, labor skills, supplier relations and linkages with customers (Subrahmanya, 2015).
The newly formed MSMEs have a huge scope to adopt technological innovation in their SC (Verhees and Meulenberg, 2004; Chaminade and Vang, 2007). Larger organizations like the original equipment manufacturers (OEMs) can share technology, knowledge and provide adequate workforce training to the MSMEs to improve their product characteristics (Kumar and Subrahmanya, 2010). MSMEs are quite prone to failure and require diligent governmental support to sustain them; hence, it is the prime responsibility of the state and the central government to ensure financial assistance to these enterprises at all times (Gupta and Barua, 2016). Various authors have proposed that there exist some unwanted government policies, just for the sake of bureaucracy, that hinder the growth of the MSMEs (Beaver and Prince, 2002; Hyland and Beckett, 2005). The government’s strategy for the growth of MSMEs should constitute technology development centers, consortiums and technology incubation centers, etc., where resources and technologies can be shared on a large scale (Hansen et al., 2009). Eco-friendly textiles and clothes with minimal adverse impact on the environment, are in high demand from various stakeholders, including the end-customers (Gardetti and Torres, 2013; Casadesus-Masanell et al., 2009; Goswami, 2008). Authors have observed that the problems faced and the practices implemented by MSMEs of the developing nations to induce sustainable behaviors within and outside their enterprises have not been extensively studied (Ciliberti et al., 2008).
There exist several barriers that hinder the execution of SSCM; however, all barriers do not have the same impact on SSCM practices. Hence, it is imperative to determine the dominant factors needed for the adoption of SSCM practices (Al Zaabi et al., 2013). First, SSCM articles were selected from several national and global publication houses such as Science Direct, Emerald Insight, Taylor and Francis, Springer, Inderscience and other openly available materials. Keyword search, such as “sustainable supply chain management, barriers to sustainable supply chain management in the textile sector,” was extensively used to identify more than 150 papers that were justifying the necessity for this study.
Based on the review of the selected articles, the barriers to the adoption of SSCP in the textile MSMEs have been enlisted in Table I.
3. Research methodology and design
Prior to the questionnaire survey, a visit to the Odisha-based textile MSME enterprises, namely, Attabira, Bolangir, Bargarh and Sambalpur, was conducted, and several field visits to various MSMEs units were carried out for data collection. Based on our experience after the visit, we identified 17 enterprises for this study. These enterprises were shortlisted based on their alignment to sustainability practices through the Confederation of Indian Industry (CII). The aim of CII is to promote healthy industrial environment. Thereafter, we mailed the 17 textile enterprises, wherein the mail included objectives and the need to conduct this kind of research. Only 12 enterprises responded and these were the ones finalized. In total, 150 stakeholders from these enterprises were selected from their databases based on their positions such as owners (12), middlemen (75) and weavers (63). They were asked to assign a number between 1 to 5 to the barriers that affected the adoption of SSCP in textile MSMEs where, 1 stood for the weakest barrier and 5 for the strongest barrier. The total number of valid responses considered for this study was 103.
A panel of 12 experts from the enterprises and academia were selected for this research. Based on their judgment, the barriers that obtained a mean value of 3.5 and above were considered for further analysis. There were 15 critical barriers that qualified this criterion and the remaining 10, which obtained less than 3.5, were ignored (Figure 1).
3.1 Interpretive structural modeling
Interpretive structural modeling (ISM) technique was first conceptualized by Warfield (1974). Several researchers utilized this technique to solve the industrial decision-making process and analyze the complexity of relationships. This technique utilizes the judgment from decision-makers based on which the linkage between the factors is determined (Mandal and Deshmukh, 1994; Gorane and Kant, 2013). ISM has been widely used across the globe with a number of applications, such as world-class manufacturing (Haleem et al., 2012), decision-making process (Yol Lee and Rhee, 2007), value chain management (Mohammed et al., 2008), product design (Lin et al., 2006), waste management (Sharma and Gupta, 1995), supply chain management (Agarwal et al., 2007) and sustainable supply chain (Diabat et al., 2014), to identify the contextual relationships between elements when they have dependencies. The technique has also been adopted for the present study because of its efficiency as a solution methodology (Attri et al., 2013). The procedural steps involved in ISM can be seen in Figure 2.
ISM technique considers judgment based on opinions from experts across enterprises and academia based on brainstorming method to establish a contextual relationship among the factors (barriers). Therefore, the team of experts has been consulted to identify the contextual relationships for this study. To evaluate the barriers, a contextual relationship of “leads to” is considered, that is one barrier leads to other.
3.1.1 Development of structural self-interaction matrix (SSIM).
While considering the contextual relationship of each barrier, the presence of a relationship among two barriers (say i, j) and the corresponding direction of the relation is considered. For this purpose, some symbols are utilized to determine the direction of the relationship among the barriers (i and j): V for barrier i leading to barrier j; A for barrier j leading to barrier i; X for barrier i and j leading to each other; and O for barrier i and j being independent.
The SSIM for barriers to the adoption of SSCP is presented in Table II.
3.1.2 Initial reachability matrix.
To develop the initial reachability matrix from SSIM, the information from SSIM is converted into binary digits of ones or zeros in the respective cells of the initial reachability matrix. This can be done by applying these rules. If an entry in the position (i, j) of the SSIM is V, then position (i, j) entry becomes 1 and position (j, i) entry becomes 0 in the initial reachability matrix; for A it becomes 0 and 1; for X it becomes 1 each; and for O it becomes 0 each (Kannan et al., 2009; Diabat et al., 2014).
The initial reachability matrix is presented in Table III.
3.1.3 Final reachability matrix.
After the initial reachability matrix is developed, transitivity rules are incorporated to fill the gap that might have occurred from the judgment collected during formation of SSIM. The transitivity rules state that if variable A is related to B and B is related to C, then A is necessarily related to C; the resultant matrix called as the final reachability matrix is obtained and is presented in Table IV.
3.1.4 Level partitions.
The reachability set and antecedent sets are identified from the final reachability matrix. While the reachability set contains the element itself and the other elements that it may influence, the antecedent set contains the element itself and the other elements that may influence it. Further, the intersection of the sets is found for all the elements and levels of all the elements are assigned. When the reachability and the intersection sets of an element are identical, that element is assigned the top most level in the ISM model. Once the top-level element is determined, it is eliminated and the same procedure is conducted to identify the elements for the next level. This process is reiterated till the level of each element is determined. The levels generate a digraph which is converted into the ISM model. Level identification process of barriers was accomplished in seven iterations presented in Table V, which depicts that the high environmental cost and lack of infrastructural development occupy the first level, and hence, would be placed at the top of the ISM based model.
3.1.5 The ISM based model.
Based on the final reachability matrix, a model is drawn as shown in Figure 3 called as the ISM model. The relationship between barriers j and i is shown by an arrow from i to j. After applying the rules of transitivity as mentioned in ISM technique, the digraph is then transformed into an ISM model for the barriers to adoption of SSCP in textile MSMEs.
3.1.6 MICMAC analysis.
Matrice d’Impacts croises-multiplication appliqúe an classment is shortened as MICMAC. The MICMAC analysis aims to differentiate the elements based on their driving power and dependence power. MICMAC is a cross-impact matrix multiplication applied to classification and utilizes the multiplication properties of matrices (Sharma and Gupta, 1995; Diabat and Govindan, 2011). It is an indirect classification that enhances the power of the matrix. Providing the classification of barriers under the different headings (direct, indirect and potential) serves as a rich source of information for the researchers. It not only highlights the importance of barriers but also gives an idea on them, which was not possible through direct classification. It is the graphical way of representation of elements (barriers) into four categories, namely, independent, linkage, autonomous, and dependent. This is conducted to identify the critical barriers that influence the system in different categories. Depending on the driving power and dependence power, barriers, in this case, have been sub-divided into four clusters as follows (Kannan et al., 2009):
Autonomous barriers: They constitute the elements having weak driving power and weak dependence power. They remain disconnected from the system, with which they have few links that might be quite strong. These barriers are located in Quadrant I.
Dependent barriers: They constitute the barriers that have weak driving power yet strong dependence power and are located in Quadrant II.
Linkage barriers: They constitute the barriers that have strong driving power and strong dependence power and are located in Quadrant III. However, they are unstable, and action on them does not affect the other elements. It also includes a feedback effect on them.
Independent barriers: They constitute the barriers that have strong driving power yet weak dependence power. These are located in Quadrant IV.
It is seen that when an element has a very strong driving power it is called as a key element, and falls into the independent or linkage cluster. The driving power and dependence power of each of these barriers are shown in Table IV. The information on barriers based on the MICMAC analysis has been discussed in great detail in the next section. The graphical representation of driving power versus dependence power is shown in Figure 4.
4. Results and discussion
Rapid industrialization has led to a scarcity of natural resources and increased global warming. This has forced enterprises to reduce the environmental impact resulting from their SC activities. These initiatives are necessary for the Indian textile units, as they deal with high level of toxic fumes and pollution. This paper evaluates the barriers to SSCP and identifies the influential barriers to adoption of SSCP. The results of this study have been visually summarized in Figures 3 and 4. In Figure 3, the ISM-based model for 15 barriers have been summarized in seven levels. From Table IV, the driving and the dependence power values generated a MICMAC analysis that provides valuable insights in relation with the 15 barriers. The present study can be interpreted as follows:
In Quadrant I, there are no barriers, which show that there are no autonomous barriers seen in the MICMAC diagram. The absence of such barriers in this study implies that all the considered barriers play a significant role in hindering the adoption of SSCP in textile MSMEs.
Quadrant II consists of barriers having low driving power and high dependence power. There are six barriers; lack of customer satisfaction (B7), inadequate infrastructural development (B9), high environmental cost (B4), lack of scope for improvement of product characteristics (B8), lack of stricter government rules and regulations (B14) and high cost of disposing hazardous wastes (B15). B7 has a dependence power of 12 and driving power of 3, which implies that there is limited pressure from the customer side for the adoption of SSCP in textile enterprises. Zhu and Sarkis (2006) have highlighted that customers need to encourage sustainable products. Similarly, B9 has dependence power 12 and driving power 2, which shows that enterprises do not invest in developing their infrastructure for sustainability. B4 has dependence power 12 and driving power 7, which shows that the environmental cost associated with adoption of eco-friendly practices have limited impact on the adoption of SSCP; similarly B8 has dependence and driving power of 12 and 7, respectively. B14 and B15 have dependence power of 10 and 11 and driving power 3 and 6, respectively, indicating that government legislations and cost of disposal of hazardous wastes have comparatively less effect on adoption of SSCP.
Eight barriers numbered (B1), (B2), (B3), (B5), (B6), (B10), (B11) and (B12) are present in Quadrant III having strong driving and strong dependence power. They are extremely unstable. Any action on these variables will affect the others in the system and also themselves, as they have a feedback effect on them (Qureshi et al., 2008).
B13, non-availability of bank loans to encourage green products is an independent barrier that lies in Quadrant IV and is placed at the bottom of the ISM hierarchy, which implies that it should be the top priority, as it has the capability to influence other barriers. It may be treated as the “major barrier” to SSCP adoption.
5. Suggestive measures
The SC managers and decision-makers must derive adequate measures to overcome the barriers, which would lead to the adoption of sustainable practices in the textile MSMEs. The autonomous barriers do not affect the performance of the textile SC. This study has not categorized any barrier as autonomous, which means all the barriers considered affect the performance of the SC. MSMEs must handle the dependent barriers with special care owing to their nature. If the linkage barriers are dealt with appropriately, there will be a positive change in the SC. As highlighted during the study, the independent factor, namely, the non-availability of bank loans to encourage green products, is one of the major barriers. With targeted, prompt and focused attention by the government for providing loans, incentives and subsidies to the sustainable textile producers would not only ameliorate, but also act as a panacea to the barriers that have been brought out in the study (Dam and Petkova, 2014).
The recent success story of the energy sector in India, which has undergone some revolutionary changes due the governmental initiatives such as green energy, cheap energy, export of energy, surplus energy, could be replicated in the textile sector as well. This would not only lead to the promotion of sustainable products but will also help in building trust among the customers (Zhu et al., 2017). Owing to the current need for conserving our ecosystem, creating awareness and campaigning about sustainable products would definitely lead to the increase in demand for such products. This would result in stable employment of the MSME employees and reduce the fear of financial losses among them. Thus, the employees would feel motivated to adopt the sustainable way of textile production. Various schemes for bringing about social sustainability of the enterprises and its’ employees must also be encouraged. As textile production involves hazardous wastes, suitable waste management approaches must be designed that could lead to effective and economic waste disposal. The introduction of environmental management system (EMS) to the enterprises could also help in determining trade-offs between environmental concern and economic stability (Darnall et al., 2008). Concerns about the health and safety of the employees and adoption of adequate economic welfare measures would create a foundation to achieve true sustainability in the textile MSMEs (Simões et al., 2013).
The SSCP should be incorporated at the very beginning of the SC, and all the stakeholders should be made responsible to attain sustainability. Extending the sustainable practices to the suppliers has been vital in achieving sustainability goals. With strong collaboration with SC stakeholders, the enterprises can work wonders to trigger economic growth with due regards to the environment and the society (Azadi et al., 2015). Adoption of green purchasing requires continuous support from the suppliers. Commitment and support from the top management to sanction funds for developing the infrastructure of the enterprises is essential (Walker and Jones, 2012; Zhu and Geng, 2013).
Encouraging the funding of the R&D activities for environmentally friendly design strategies and technologies requires attention (Klassen, 2001). The product characteristics need to be improved, which would not only meet the customer demand but also satisfy the sustainability criteria. Proper training and educating the MSME employees is also important, as there is a lack of environmental knowledge and the benefits associated with adoption of sustainable means for manufacturing (Bowen and Chen, 2001; Hutchins and Sutherland, 2008). Moreover, it is imperative to design effective performance assessment systems to measure the sustainable standards adopted by the suppliers (Govindan et al., 2015; Dubey et al., 2015).
From the current study, it is observed that textile enterprises are certainly aware of environmental issues and are keen on satisfying their customers by improving environmental performance by adopting SSCP into their SCs. However, identification of the leading barriers for SSCM has been a challenge to the researchers.
It is clear from this research that identification of leading barriers in textile MSMEs is crucial for the adoption of SSCP, and this issue has been elaborately explained in the present study by applying the ISM technique. The analysis not only suggests enhancing the ecological performance but also the promotion of a sustainable textile zone. The study is insightful to textile MSMEs of Odisha. This study provides a framework, which would enable the adoption of SSCM in textile MSMEs. It is critical for the enterprises to imbibe environmental consciousness.
This study was conducted in the Odisha-based textile MSMEs; hence, including more enterprises would give a more robust solution. This study could also be extended beyond the textile sector. A total of 15 barriers were considered in this study; however, in reality, there exist more of them. In future, multi-criteria decision-making approaches (AHP/ANP) could be used to prioritize the barriers along with ISM.
A list of barriers to SSCPs
Structural self-interaction matrix (SSIM)
Initial reachability matrix
Final reachability matrix
Level partitions for barriers
|Barriers||Reachability set||Antecedent||Interaction||Iteration no and level|
Agarwal, A., Shankar, R. and Tiwari, M.K. (2007), “Modeling agility of supply chain”, Industrial Marketing Management, Vol. 36 No. 4, pp. 443-457.
Al Zaabi, S., Al Dhaheri, N. and Diabat, A. (2013), “Analysis of interaction between the barriers for the implementation of sustainable supply chain management”, The International Journal of Advanced Manufacturing Technology, Vol. 68 Nos 1/4, pp. 895-905.
Angelis-Dimakis, A., Arampatzis, G. and Assimacopoulos, D. (2016), “Systemic eco-efficiency assessment of meso-level water use systems”, Journal of Cleaner Production, Vol. 138, pp. 195-207.
Atasu, A. (2016), Environmentally Responsible Supply Chains.
Attri, R., Dev, N. and Sharma, V. (2013), “Interpretive structural modelling (ISM) approach: an overview”, Research Journal of Management Sciences, Vol. 2 No. 2, pp. 3-8.
Azadi, M., Jafarian, M., Saen, R.F. and Mirhedayatian, S.M. (2015), “A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context”, Computers & Operations Research, Vol. 54, pp. 274-285.
Beaver, G. and Prince, C. (2002), “Innovation, entrepreneurship and competitive advantage in the entrepreneurial venture”, Journal of Small Business and Enterprise Development, Vol. 9 No. 1, pp. 28-37.
Beton, A., Dias, D., Farrant, L., Gibon, T., le Guern, Y., Desaxce, M. and Boufateh, I. (2011), “Environmental improvement potential of textiles (IMPRO-Textiles)”, JRC Scientific and Technical Reports, join study by European Commission, Bio Intelligence Service, and ENSAIT, p. 190.
Bhaskaran, S., Polonsky, M., Cary, J. and Fernandez, S. (2006), “Environmentally sustainable food production and marketing: opportunity or hype?”, British Food Journal, Vol. 108 No. 8, pp. 677-690.
Börjeson, N., Gilek, M. and Karlsson, M. (2015), “Knowledge challenges for responsible supply chain management of chemicals in textiles – as experienced by procuring organisations”, Journal of Cleaner Production, Vol. 107, pp. 130-136.
Bostrom, D., Skoglund, N., Grimm, A., Boman, C., Ohman, M., Brostrom, M. and Backman, R. (2011), “Ash transformation chemistry during combustion of biomass”, Energy & Fuels, Vol. 26 No. 1, pp. 85-93.
Bowen, J.T. and Chen, S.L. (2001), “The relationship between customer loyalty and customer satisfaction”, International Journal of Contemporary Hospitality Management, Vol. 13 No. 5, pp. 213-217.
Carter, C.R. and Dresner, M. (2001), “Purchasing’s role in environmental management: cross‐functional development of grounded theory”, The Journal of Supply Chain Management, Vol. 37 No. 3, pp. 12-27.
Carter, C.R. and Rogers, D.S. (2008), “A framework of sustainable supply chain management: moving toward new theory”, International Journal of Physical Distribution & Logistics Management, Vol. 38 No. 5, pp. 360-387.
Casadesus-Masanell, R., Crooke, M., Reinhardt, F. and Vasishth, V. (2009), “Households’ willingness to pay for “green” goods: evidence from Patagonia’s introduction of organic cotton sportswear”, Journal of Economics & Management Strategy, Vol. 18 No. 1, pp. 203-233.
Chaminade, C. and Vang, J. (2007), “Innovation policies for Asian SMEs: an innovation system perspective”, Handbook of Research on Asian Business, Vol. 19, p. 381.
Christmann, P. (2000), “Effects of best practices of environmental management on cost advantage: the role of complementary assets”, Academy of Management Journal, Vol. 43 No. 4, pp. 663-680.
Ciliberti, F., Pontrandolfo, P. and Scozzi, B. (2008), “Investigating corporate social responsibility in supply chains: a SME perspective”, Journal of Cleaner Production, Vol. 16 No. 15, pp. 1579-1588.
Dam, L. and Petkova, B.N. (2014), “The impact of environmental supply chain sustainability programs on shareholder wealth”, International Journal of Operations & Production Management, Vol. 43 No. 5, pp. 586-609.
Darnall, N., Jolley, G.J. and Handfield, R. (2008), “Environmental management systems and green supply chain management: complements for sustainability?”, Business Strategy and the Environment, Vol. 17 No. 1, pp. 30-45.
Dasgupta, J., Sikder, J., Chakraborty, S., Curcio, S. and Drioli, E. (2015), “Remediation of textile effluents by membrane based treatment techniques: a state of the art review”, Journal of Environmental Management, Vol. 147, pp. 55-72.
Defra, A. (2008), Framework for Pro-Environmental Behaviours, Department for Environment, Food and Rural Affairs, London.
Diabat, A. and Govindan, K. (2011), “An analysis of the drivers affecting the implementation of green supply chain management”, Resources, Conservation and Recycling, Vol. 55 No. 6, pp. 659-667.
Diabat, A., Kannan, D. and Mathiyazhagan, K. (2014), “Analysis of enablers for implementation of sustainable supply chain management–a textile case”, Journal of Cleaner Production, Vol. 83, pp. 391-403.
Draper, P., Halleson, D. and Alves, P. (2007), “SACU, Regional Integration and the Overlap Issue in Southern Africa”, No. 15, Trade Policy Report.
Dubey, R., Gunasekaran, A., Papadopoulos, T. and Childe, S.J. (2015), “Green supply chain management enablers: mixed methods research”, Sustainable Production and Consumption, Vol. 4, pp. 72-88.
Elkington, J. (1994), “Towards the sustainable corporation: win-win-win business strategies for sustainable development”, California Management Review, Vol. 36 No. 2, pp. 90-100.
Eniola, A.A. and Entebang, H. (2015), “Government policy and performance of small and medium business management”, International Journal of Academic Research in Business and Social Sciences, Vol. 5 No. 2, p. 237.
Epstein, M.J. and Roy, M.J. (2000), “Strategic evaluation of environmental projects in SMEs”, Environmental Quality Management, Vol. 9 No. 3, pp. 37-47.
Eriksson, T., Smeets, V. and Warzynski, F. (2009), “Small open economy firms in international trade: Evidence from Danish transactions-level data”, Nationaløkonomisk tidsskrift, Vol. 147 No. 2, p. 175.
Fletcher, K. (2013), Sustainable Fashion and Textiles: Design Journeys, Routledge.
Gabzdylova, B., Raffensperger, J.F. and Castka, P. (2009), “Sustainability in the New Zealand wine industry: drivers, stakeholders and practices”, Journal of Cleaner Production, Vol. 17 No. 11, pp. 992-998.
Gardetti, M.A. and Torres, A.L. (Eds), (2013), Sustainability in Fashion and Textiles: Values, Design, Production and Consumption, Greenleaf Publishing.
Goodland, R. (1995), “The concept of environmental sustainability”, Annual Review of Ecology and Systematics, Vol. 26 No. 1, pp. 1-24.
Gorane, S.J. and Kant, R. (2013), “Supply chain management: modelling the enablers using ISM and fuzzy MICMAC approach”, International Journal of Logistics Systems and Management, Vol. 16 No. 2, pp. 147-166.
Goswami, P. (2008), “Is the urban Indian consumer ready for clothing with eco-labels?”, International Journal of Consumer Studies, Vol. 32 No. 5, pp. 438-446.
Govindan, K., Khodaverdi, R. and Jafarian, A. (2013), “A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach”, Journal of Cleaner Production, Vol. 47, pp. 345-354.
Govindan, K., Rajendran, S., Sarkis, J. and Murugesan, P. (2015), “Multi criteria decision making approaches for green supplier evaluation and selection: a literature review”, Journal of Cleaner Production, Vol. 98, pp. 66-83.
Gupta, H. and Barua, M.K. (2016), “Identifying enablers of technological innovation for Indian MSMEs using best–worst multi criteria decision making method”, Technological Forecasting and Social Change, Vol. 107, pp. 69-79.
Gwilt, A. and Rissanen, T. (2011), Shaping Sustainable Fashion: Changing the Way We Make and Use Clothes, Routledge.
Haleem, A., Sushil, Qadri, M.A. and Kumar, S. (2012), “Analysis of critical success factors of world-class manufacturing practices: an application of interpretative structural modelling and interpretative ranking process”, Production Planning & Control, Vol. 23 Nos 10/11, pp. 722-734.
Hansen, H., Rand, J. and Tarp, F. (2009), “Enterprise growth and survival in Vietnam: does government support matter?”, The Journal of Development Studies, Vol. 45 No. 7, pp. 1048-1069.
Hasanbeigi, A., Price, L. and Lin, E. (2012), “Emerging energy-efficiency and CO 2 emission-reduction technologies for cement and concrete production: a technical review”, Renewable and Sustainable Energy Reviews, Vol. 16 No. 8, pp. 6220-6238.
Hillary, R. (Ed.), (2000), Small and Medium-Sized Enterprises and the Environment: Business Imperatives, Greenleaf Publishing.
Hilson, G. (2000), “Barriers to implementing cleaner technologies and cleaner production (CP) practices in the mining industry: a case study of the Americas”, Minerals Engineering, Vol. 13 No. 7, pp. 699-717.
Hubacek, K., Guan, D. and Barua, A. (2007), “Changing lifestyles and consumption patterns in developing countries: a scenario analysis for China and India”, Futures, Vol. 39 No. 9, pp. 1084-1096.
Hussain, M. (2011), “Modelling the enablers and alternatives for sustainable supply chain management”, Doctoral dissertation, Concordia University.
Hussey, D.M. and Eagan, P.D. (2007), “Using structural equation modeling to test environmental performance in small and medium-sized manufacturers: can SEM help SMEs?”, Journal of Cleaner Production, Vol. 15 No. 4, pp. 303-312.
Hutchins, M.J. and Sutherland, J.W. (2008), “An exploration of measures of social sustainability and their application to supply chain decisions”, Journal of Cleaner Production, Vol. 16 No. 15, pp. 1688-1698.
Hyland, P. and Beckett, R. (2005), “Engendering an innovative culture and maintaining operational balance”, Journal of Small Business and Enterprise Development, Vol. 12 No. 3, pp. 336-352.
Kannan, G., Pokharel, S. and Kumar, P.S. (2009), “A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider”, Resources, Conservation and Recycling, Vol. 54 No. 1, pp. 28-36.
Klassen, R.D. and Whybark, D.C. (1999), “The impact of environmental technologies on manufacturing performance”, Academy of Management Journal, Vol. 42 No. 6, pp. 599-615.
Klassen, R.D. (2001), “Plant‐level environmental management orientation: the influence of management views and plant characteristics”, Production and Operations Management, Vol. 10 No. 3, pp. 257-275.
Kleindorfer, P.R., Singhal, K. and Wassenhove, L.N. (2005), “Sustainable operations management”, Production and Operations Management, Vol. 14 No. 4, pp. 482-492.
Kocabas, A.M., Yukseler, H., Dilek, F.B. and Yetis, U. (2009), “Adoption of European union’s IPPC directive to a textile mill: analysis of water and energy consumption”, Journal of Environmental Management, Vol. 91 No. 1, pp. 102-113.
Kumar, R.S. and Subrahmanya, M.B. (2010), “Influence of subcontracting on innovation and economic performance of SMEs in Indian automobile industry”, Technovation, Vol. 30 No. 11, pp. 558-569.
Lai, K.H., Bao, Y. and Li, X. (2008), “Channel relationship and business uncertainty: evidence from the Hong Kong market”, Industrial Marketing Management, Vol. 37 No. 6, pp. 713-724.
Lin, C.Y. and Ho, Y.H. (2008), “An empirical study on logistics service providers’ intention to adopt green innovations”, Journal of Technology Management & Innovation, Vol. 3 No. 1, pp. 17-26.
Lin, M.C., Wang, C.C. and Chen, T.C. (2006), “A strategy for managing customer-oriented product design”, Concurrent Engineering, Vol. 14 No. 3, pp. 231-244.
Luthra, S. and Haleem, A. (2015), “Hurdles in implementing sustainable supply chain management: an analysis of Indian automobile sector”, Procedia-Social and Behavioral Sciences, Vol. 189, pp. 175-183.
Madrid-Guijarro, A., Garcia, D. and Van Auken, H. (2009), “Barriers to innovation among Spanish manufacturing SMEs”, Journal of Small Business Management, Vol. 47 No. 4, pp. 465-488.
Mandal, A. and Deshmukh, S.G. (1994), “Vendor selection using interpretive structural modelling (ISM)”, International Journal of Operations & Production Management, Vol. 14 No. 6, pp. 52-59.
Marsillac, E.L. (2008), “Environmental impacts on reverse logistics and green supply chains: similarities and integration”, International Journal of Logistics Systems and Management, Vol. 4 No. 4, pp. 411-422.
Mathiyazhagan, K., Govindan, K., NoorulHaq, A. and Geng, Y. (2013), “An ISM approach for the barrier analysis in implementing green supply chain management”, Journal of Cleaner Production, Vol. 47, pp. 283-297.
Mohammed, I.R., Shankar, R. and Banwet, D.K. (2008), “Creating flex-lean-agile value chain by outsourcing: an ISM-based interventional roadmap”, Business Process Management Journal, Vol. 14 No. 3, pp. 338-389.
Mohanty, R.P. and Prakash, A. (2014), “Green supply chain management practices in India: an empirical study”, Production Planning & Control, Vol. 25 No. 16, pp. 1322-1337.
Morgan, A. (2006), “Teaching geography for a sustainable future”.
Mudgal, R.K., Shankar, R., Talib, P. and Raj, T. (2009), “Greening the supply chain practices: an Indian perspective of enablers’ relationships”, International Journal of Advanced Operations Management, Vol. 1 Nos 2/3, pp. 151-176.
Mudgal, R.K., Shankar, R., Talib, P. and Raj, T. (2010), “Modelling the barriers of green supply chain practices: an Indian perspective”, International Journal of Logistics Systems and Management, Vol. 7 No. 1, pp. 81-107.
Nanda, T. and Singh, T.P. (2009), “An assessment of the technology innovation initiatives in the Indian small-scale manufacturing industry”, International Journal of Technology, Policy and Management, Vol. 9 No. 2, pp. 173-207.
Nishat Faisal, M. (2010), “Sustainable supply chains: a study of interaction among the enablers”, Business Process Management Journal, Vol. 16 No. 3, pp. 508-529.
Nishat Faisal, M., Banwet, D.K. and Shankar, R. (2007), “Information risks management in supply chains: an assessment and mitigation framework”, Journal of Enterprise Information Management, Vol. 20 No. 6, pp. 677-699.
Ofori, G. (2000), “Challenges of construction industries in developing countries: lessons from various countries”, 2nd International Conference on Construction in Developing Countries: Challenges Facing the Construction Industry in Developing Countries, Gaborone, November, pp. 15-17.
Perron, G.M. (2005), Barriers to Environmental Performance Improvements in Canadian SMEs, Dalhousie University, Canada.
Qureshi, M.N., Kumar, D. and Kumar, P. (2008), “An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers”, Asia Pacific Journal of Marketing and Logistics, Vol. 20 No. 2, pp. 227-249.
Rao, P. and Holt, D. (2005), “Do green supply chains lead to competitiveness and economic performance?”, International Journal of Operations & Production Management, Vol. 25 No. 9, pp. 898-916.
Ravi, V. and Shankar, R. (2005), “Analysis of interactions among the barriers of reverse logistics”, Technological Forecasting and Social Change, Vol. 72 No. 8, pp. 1011-1029.
Reddy, N., Chen, L., Zhang, Y. and Yang, Y. (2014), “Reducing environmental pollution of the textile industry using keratin as alternative sizing agent to poly (vinyl alcohol)”, Journal of Cleaner Production, Vol. 65, pp. 561-567.
Revell, A. and Rutherfoord, R. (2003), “UK environmental policy and the small firm: broadening the focus”, Business Strategy and the Environment, Vol. 12 No. 1, pp. 26-35.
Rowe, J. and Enticott, R. (1998), “The role of local authorities in improving the environmental management of SMEs: some observations from partnership programmes in the west of England”, Eco-Management and Auditing, Vol. 5 No. 2, pp. 75-87.
Rutherfoord, R., Blackburn, R.A. and Spence, L.J. (2000), “Environmental management and the small firm”, An International Journal of Entrepreneurial Behavior & Research, Vol. 6 No. 6, pp. 310-326.
Saad, G.H. and Siha, S. (2000), “Managing quality: critical links and a contingency model”, International Journal of Operations & Production Management, Vol. 20 No. 10, pp. 1146-1164.
Sarkis, J. (2012), “A boundaries and flows perspective of green supply chain management”, Supply Chain Management: An International Journal, Vol. 17 No. 2, pp. 202-216.
Sarkis, J., Helms, M.M. and Hervani, A.A. (2010), “Reverse logistics and social sustainability”, Corporate Social Responsibility and Environmental Management, Vol. 17 No. 6, pp. 337-354.
Scruggs, C.E. and Ortolano, L. (2011), “Creating safer consumer products: the information challenges companies face”, Environmental Science & Policy, Vol. 14 No. 6, pp. 605-614.
Sharma, H.D. and Gupta, A.D. (1995), “The objectives of waste management in India: a futures inquiry”, Technological Forecasting and Social Change, Vol. 48 No. 3, pp. 285-309.
Shen, L.Y. and Tam, V.W. (2002), “Implementation of environmental management in the Hong Kong construction industry”, International Journal of Project Management, Vol. 20 No. 7, pp. 535-543.
Shrivastava, P. (1995), “The role of corporations in achieving ecological sustainability”, Academy of Management Review, Vol. 20 No. 4, pp. 936-960.
Simões, J., Redondo, R.D. and Vilas, A.F. (2013), “A social gamification framework for a K-6 learning platform”, Computers in Human Behavior, Vol. 29 No. 2, pp. 345-353.
Siong Kuik, S., Verl Nagalingam, S. and Amer, Y. (2010), “Sustainable supply chain for collaborative manufacturing”, Journal of Manufacturing Technology Management, Vol. 22 No. 8, pp. 984-1001.
Subrahmanya, M.B. (2005), “Pattern of technological innovations in small enterprises: a comparative perspective of Bangalore (India) and northeast England (UK)”, Technovation, Vol. 25 No. 3, pp. 269-280.
Subrahmanya, M.B. (2015), “Innovation and growth of engineering SMEs in Bangalore: why do only some innovate and only some grow faster?”, Journal of Engineering and Technology Management, Vol. 36, pp. 24-40.
Svensson, G. (2007), “Aspects of sustainable supply chain management (SSCM): conceptual framework and empirical example”, Supply Chain Management: An International Journal, Vol. 12 No. 4, pp. 262-266.
Tilley, F. (1999), “The gap between the environmental attitudes and the environmental behaviour of small firms”, Business Strategy and the Environment, Vol. 8 No. 4, p. 238.
Vajnhandl, S. and Valh, J.V. (2014), “The status of water reuse in European textile sector”, Journal of Environmental Management, Vol. 141, pp. 29-35.
Van Hemel, C. and Cramer, J. (2002), “Barriers and stimuli for eco-design in SMEs”, Journal of Cleaner Production, Vol. 10 No. 5, pp. 439-453.
Verhees, F.J. and Meulenberg, M.T. (2004), “Market orientation, innovativeness, product innovation, and performance in small firms”, Journal of Small Business Management, Vol. 42 No. 2, pp. 134-154.
Vojdani, N. and Lootz, F. (2012), “Designing supply chain networks for the offshore wind energy industry”, International Journal of Business Performance and Supply Chain Modelling, Vol. 4 Nos 3/4, pp. 271-284.
Waheed, B., Khan, F. and Veitch, B. (2009), “Linkage-based frameworks for sustainability assessment: making a case for driving force-pressure-state-exposure-effect-action (DPSEEA) frameworks”, Sustainability, Vol. 1 No. 4, pp. 441-463.
Warfield, J.N. (1974), “Developing subsystem matrices in structural modeling”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 4 No. 1, pp. 74-80.
Williams, S. and Schaefer, A. (2013), “Small and medium‐sized enterprises and sustainability: managers’ values and engagement with environmental and climate change issues”, Business Strategy and the Environment, Vol. 22 No. 3, pp. 173-186.
Walker, H. and Jones, N. (2012), “Sustainable supply chain management across the UK private sector”, Supply Chain Management: An International Journal, Vol. 17 No. 1, pp. 15-28.
Yol Lee, S. and Rhee, S.K. (2007), “The change in corporate environmental strategies: a longitudinal empirical study”, Management Decision, Vol. 45 No. 2, pp. 196-216.
Zhu, Q. and Geng, Y. (2013), “Drivers and barriers of extended supply chain practices for energy saving and emission reduction among Chinese manufacturers”, Journal of Cleaner Production, Vol. 40, pp. 6-12.
Zhu, Q. and Sarkis, J. (2006), “An inter-sectoral comparison of green supply chain management in China: drivers and practices”, Journal of Cleaner Production, Vol. 14 No. 5, pp. 472-486.
Zhu, Q., Feng, Y. and Choi, S.B. (2017), “The role of customer relational governance in environmental and economic performance improvement through green supply chain management”, Journal of Cleaner Production, Vol. 155, pp. 46-53.
Ansari, Z.N. and Kant, R. (2017), “A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management”, Journal of Cleaner Production, Vol. 142, pp. 2524-2543.
Hopkins, M.S., Townend, A., Khayat, Z., Balagopal, B., Reeves, M. and Berns, M. (2009), “The business of sustainability: what it means to managers now”, MIT Sloan Management Review, Vol. 51 No. 1, p. 20.
Jansen, L. (2003), “The challenge of sustainable development”, Journal of Cleaner Production, Vol. 11 No. 3, pp. 231-245.
Jia, P., Diabat, A. and Mathiyazhagan, K. (2015), “Analyzing the SSCM practices in the mining and mineral industry by ISM approach”, Resources Policy, Vol. 46, pp. 76-85.
Nayak, S.S. and Mahapatra, B. (2016), “Growth and prospect of MSMES in Odisha: an analytical approach”, PARIPEX-Indian Journal of Research, Vol. 5 No. 2.
Nidumolu, R., Prahalad, C.K. and Rangaswami, M.R. (2009), “Why sustainability is now the key driver of innovation”, Harvard Business Review, Vol. 87 No. 9, pp. 56-64.
Swee, S.K., Sev, V.N. and Amer, Y. (2010), “Challenges in implementing sustainable supply chain within a collaborative manufacturing network”, 8th International Conference on In Supply Chain Management and Information Systems (SCMIS), IEEE, pp. 1-8.
Zhu, Q., Geng, Y., Fujita, T. and Hashimoto, S. (2010), “Green supply chain management in leading manufacturers: case studies in Japanese large companies”, Management Research Review, Vol. 33 No. 4, pp. 380-392.